To run this code in my project using the renv environment, run the following lines of code
install.packages("renv") #install the package on the new computer (may not be necessary if renv bootstraps itself as expected)
renv::restore() #reinstall all the package versions in the renv lockfile
require("genefilter")
## Loading required package: genefilter
require("DESeq2")
## Loading required package: DESeq2
## Loading required package: S4Vectors
## Loading required package: stats4
## Loading required package: BiocGenerics
##
## Attaching package: 'BiocGenerics'
## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs
## The following objects are masked from 'package:base':
##
## anyDuplicated, aperm, append, as.data.frame, basename, cbind,
## colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
## get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
## match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
## Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
## table, tapply, union, unique, unsplit, which.max, which.min
##
## Attaching package: 'S4Vectors'
## The following object is masked from 'package:utils':
##
## findMatches
## The following objects are masked from 'package:base':
##
## expand.grid, I, unname
## Loading required package: IRanges
## Loading required package: GenomicRanges
## Loading required package: GenomeInfoDb
## Loading required package: SummarizedExperiment
## Loading required package: MatrixGenerics
## Loading required package: matrixStats
## Warning: package 'matrixStats' was built under R version 4.3.3
##
## Attaching package: 'matrixStats'
## The following objects are masked from 'package:genefilter':
##
## rowSds, rowVars
##
## Attaching package: 'MatrixGenerics'
## The following objects are masked from 'package:matrixStats':
##
## colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
## colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
## colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
## colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
## colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
## colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
## colWeightedMeans, colWeightedMedians, colWeightedSds,
## colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
## rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
## rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
## rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
## rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
## rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
## rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
## rowWeightedSds, rowWeightedVars
## The following objects are masked from 'package:genefilter':
##
## rowSds, rowVars
## Loading required package: Biobase
## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.
##
## Attaching package: 'Biobase'
## The following object is masked from 'package:MatrixGenerics':
##
## rowMedians
## The following objects are masked from 'package:matrixStats':
##
## anyMissing, rowMedians
require("apeglm")
## Loading required package: apeglm
require("ashr")
## Loading required package: ashr
require("ggplot2")
## Loading required package: ggplot2
require("vsn")
## Loading required package: vsn
require("hexbin")
## Loading required package: hexbin
## Warning: package 'hexbin' was built under R version 4.3.3
require("pheatmap")
## Loading required package: pheatmap
require("RColorBrewer")
## Loading required package: RColorBrewer
require("EnhancedVolcano")
## Loading required package: EnhancedVolcano
## Loading required package: ggrepel
## Warning: package 'ggrepel' was built under R version 4.3.3
require("tidyverse")
## Loading required package: tidyverse
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ lubridate::%within%() masks IRanges::%within%()
## ✖ dplyr::collapse() masks IRanges::collapse()
## ✖ dplyr::combine() masks Biobase::combine(), BiocGenerics::combine()
## ✖ dplyr::count() masks matrixStats::count()
## ✖ dplyr::desc() masks IRanges::desc()
## ✖ tidyr::expand() masks S4Vectors::expand()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::first() masks S4Vectors::first()
## ✖ dplyr::lag() masks stats::lag()
## ✖ ggplot2::Position() masks BiocGenerics::Position(), base::Position()
## ✖ purrr::reduce() masks GenomicRanges::reduce(), IRanges::reduce()
## ✖ dplyr::rename() masks S4Vectors::rename()
## ✖ lubridate::second() masks S4Vectors::second()
## ✖ lubridate::second<-() masks S4Vectors::second<-()
## ✖ dplyr::slice() masks IRanges::slice()
## ✖ readr::spec() masks genefilter::spec()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
sessionInfo() #provides list of loaded packages and version of R.
## R version 4.3.2 (2023-10-31)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.0
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats4 stats graphics grDevices datasets utils methods
## [8] base
##
## other attached packages:
## [1] lubridate_1.9.3 forcats_1.0.0
## [3] stringr_1.5.1 dplyr_1.1.4
## [5] purrr_1.0.2 readr_2.1.5
## [7] tidyr_1.3.1 tibble_3.2.1
## [9] tidyverse_2.0.0 EnhancedVolcano_1.18.0
## [11] ggrepel_0.9.6 RColorBrewer_1.1-3
## [13] pheatmap_1.0.12 hexbin_1.28.5
## [15] vsn_3.68.0 ggplot2_3.5.1
## [17] ashr_2.2-63 apeglm_1.22.1
## [19] DESeq2_1.40.2 SummarizedExperiment_1.30.2
## [21] Biobase_2.60.0 MatrixGenerics_1.12.3
## [23] matrixStats_1.4.1 GenomicRanges_1.54.1
## [25] GenomeInfoDb_1.36.4 IRanges_2.34.1
## [27] S4Vectors_0.38.2 BiocGenerics_0.46.0
## [29] genefilter_1.82.1
##
## loaded via a namespace (and not attached):
## [1] DBI_1.2.3 bitops_1.0-9 rlang_1.1.4
## [4] magrittr_2.0.3 compiler_4.3.2 RSQLite_2.3.9
## [7] png_0.1-8 vctrs_0.6.5 pkgconfig_2.0.3
## [10] crayon_1.5.3 fastmap_1.2.0 XVector_0.40.0
## [13] utf8_1.2.4 rmarkdown_2.28 tzdb_0.4.0
## [16] preprocessCore_1.62.1 bit_4.5.0 xfun_0.48
## [19] zlibbioc_1.46.0 cachem_1.1.0 jsonlite_1.8.9
## [22] blob_1.2.4 DelayedArray_0.26.7 BiocParallel_1.34.2
## [25] irlba_2.3.5.1 parallel_4.3.2 R6_2.5.1
## [28] stringi_1.8.4 bslib_0.8.0 limma_3.56.2
## [31] SQUAREM_2021.1 jquerylib_0.1.4 numDeriv_2016.8-1.1
## [34] Rcpp_1.0.13-1 knitr_1.48 timechange_0.3.0
## [37] Matrix_1.6-5 splines_4.3.2 tidyselect_1.2.1
## [40] rstudioapi_0.17.0 abind_1.4-8 yaml_2.3.10
## [43] codetools_0.2-20 affy_1.78.2 lattice_0.22-6
## [46] plyr_1.8.9 withr_3.0.1 KEGGREST_1.40.1
## [49] coda_0.19-4.1 evaluate_1.0.1 survival_3.7-0
## [52] Biostrings_2.70.3 pillar_1.9.0 affyio_1.70.0
## [55] BiocManager_1.30.25 renv_1.0.11 generics_0.1.3
## [58] invgamma_1.1 RCurl_1.98-1.16 truncnorm_1.0-9
## [61] emdbook_1.3.13 hms_1.1.3 munsell_0.5.1
## [64] scales_1.3.0 xtable_1.8-4 glue_1.8.0
## [67] tools_4.3.2 annotate_1.78.0 locfit_1.5-9.10
## [70] mvtnorm_1.3-2 XML_3.99-0.17 grid_4.3.2
## [73] bbmle_1.0.25.1 bdsmatrix_1.3-7 AnnotationDbi_1.64.1
## [76] colorspace_2.1-1 GenomeInfoDbData_1.2.10 cli_3.6.3
## [79] fansi_1.0.6 mixsqp_0.3-54 S4Arrays_1.0.6
## [82] gtable_0.3.5 sass_0.4.9 digest_0.6.37
## [85] memoise_2.0.1 htmltools_0.5.8.1 lifecycle_1.0.4
## [88] httr_1.4.7 bit64_4.5.2 MASS_7.3-60.0.1
save_ggplot <- function(plot, filename, width = 10, height = 7, units = "in", dpi = 300) {
# Display plot
print(plot)
# Save plot
ggsave(filename = paste0(filename, ".png"), plot = plot, width = width, height = height, units = units, dpi = dpi)
}
# Specify colors
ann_colors = list(
Tissue = c(OralEpi = "palegreen3" ,Aboral = "mediumpurple1")
)
Read in raw count data
counts_raw <- read.csv("../output_RNA/stringtie-GeneExt/LCM_RNA_gene_count_matrix.csv", row.names = 1) #load in data
gene_id,LCM_15,LCM_16,LCM_20,LCM_21,LCM_26,LCM_27,LCM_4,LCM_5,LCM_8,LCM_9
Read in metadata
meta <- read.csv("../data_RNA/LCM_RNA_metadata.csv") %>%
dplyr::arrange(Sample) %>%
mutate(across(c(Tissue, Fragment, Section_Date, LCM_Date), factor)) # Set variables as factors
meta$Tissue <- factor(meta$Tissue, levels = c("OralEpi","Aboral")) #we want OralEpi to be the baseline
Data sanity checks!
all(meta$Sample %in% colnames(counts_raw)) #are all of the sample names in the metadata column names in the gene count matrix? Should be TRUE
## [1] TRUE
all(meta$Sample == colnames(counts_raw)) #are they the same in the same order? Should be TRUE
## [1] TRUE
ffun<-filterfun(pOverA(0.5,10)) #Keep genes expressed in at least 50% of samples -
counts_filt_poa <- genefilter((counts_raw), ffun) #apply filter
filtered_counts <- counts_raw[counts_filt_poa,] #keep only rows that passed filter
cat("Number of genes after filtering:", sum(counts_filt_poa))
## Number of genes after filtering: 14464
write.csv(filtered_counts, "../output_RNA/differential_expression/filtered_counts.csv")
There are now 14464 genes in the filtered dataset.
Data sanity checks:
all(meta$Sample %in% colnames(filtered_counts)) #are all of the sample names in the metadata column names in the gene count matrix?
## [1] TRUE
all(meta$Sample == colnames(filtered_counts)) #are they the same in the same order? Should be TRUE
## [1] TRUE
Create DESeq object and run DESeq2
dds <- DESeqDataSetFromMatrix(countData = filtered_counts,
colData = meta,
design= ~ Fragment + Tissue)
dds <- DESeq(dds)
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
### Extract results for Aboral vs. OralEpi contrast
res <- results(dds, contrast = c("Tissue","Aboral","OralEpi"))
resLFC <- lfcShrink(dds, coef="Tissue_Aboral_vs_OralEpi", res=res, type = "apeglm")
## using 'apeglm' for LFC shrinkage. If used in published research, please cite:
## Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
## sequence count data: removing the noise and preserving large differences.
## Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895
res <- resLFC
resOrdered <- res[order(res$pvalue),]# save differentially expressed genes
DE_05 <- as.data.frame(resOrdered) %>% filter(padj < 0.05)
DE_05_Up <- DE_05 %>% filter(log2FoldChange > 0) #Higher in Aboral, Lower in OralEpi
DE_05_Down <- DE_05 %>% filter(log2FoldChange < 0) #Lower in Aboral, Higher in OralEpi
nrow(DE_05)
## [1] 3606
nrow(DE_05_Up) #Higher in Aboral, Lower in OralEpi
## [1] 804
nrow(DE_05_Down) #Lower in Aboral, Higher in OralEpi
## [1] 2802
write.csv(as.data.frame(resOrdered),
file="../output_RNA/differential_expression/DESeq_results.csv")
write.csv(DE_05,
file="../output_RNA/differential_expression/DEG_05.csv")
EnhancedVolcano(resLFC,
lab = rownames(resLFC),
x = 'log2FoldChange',
y = 'pvalue')
plotMA(results(dds, contrast = c("Tissue","Aboral","OralEpi")), ylim=c(-20,20))
plotMA(resLFC, ylim=c(-20,20))
# because we are interested in the comparison and not the intercept, we set 'coef=2'
resNorm <- lfcShrink(dds, coef=6, type="normal")
## using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).
##
## Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
## See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
## Reference: https://doi.org/10.1093/bioinformatics/bty895
resAsh <- lfcShrink(dds, coef=6, type="ashr")
## using 'ashr' for LFC shrinkage. If used in published research, please cite:
## Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
## https://doi.org/10.1093/biostatistics/kxw041
par(mfrow=c(1,3), mar=c(4,4,2,1))
xlim <- c(1,1e5); ylim <- c(-20,20)
plotMA(resLFC, xlim=xlim, ylim=ylim, main="apeglm")
plotMA(resNorm, xlim=xlim, ylim=ylim, main="normal")
plotMA(resAsh, xlim=xlim, ylim=ylim, main="ashr")
plotCounts(dds, gene=which.max(res$log2FoldChange), intgroup="Tissue")
plotCounts(dds, gene=which.min(res$log2FoldChange), intgroup="Tissue")
Transforming count data for visualization
vsd <- vst(dds, blind=FALSE)
rld <- rlog(dds, blind=FALSE)
ntd <- normTransform(dds) # this gives log2(n + 1)
meanSdPlot(assay(vsd), main = "vsd")
## Warning in geom_hex(bins = bins, ...): Ignoring unknown parameters: `main`
meanSdPlot(assay(rld))
meanSdPlot(assay(ntd))
#save the vsd transformation
vsd_mat <- assay(vsd)
write.csv(vsd_mat, file = "../output_RNA/differential_expression/vsd_expression_matrix.csv")
Will move forward with vst transformation for visualizations
df <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])
#view all genes
pheatmap(assay(vsd), cluster_rows=TRUE, show_rownames=FALSE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)),
annotation_colors = ann_colors,color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200))
#view highest count genes
select <- order(rowMeans(counts(dds,normalized=TRUE)),
decreasing=TRUE)[1:20]
pheatmap(assay(vsd)[select,], cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)),
annotation_colors = ann_colors, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200))
#view most significantly differentially expressed genes
select <- order(res$padj)[1:20]
pheatmap(assay(vsd)[select,], cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2, annotation_col=(df%>% select(Tissue)),
annotation_colors = ann_colors,color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200))
sampleDists <- dist(t(assay(vsd)))
sampleDistMatrix <- as.matrix(sampleDists)
rownames(sampleDistMatrix) <- paste(vsd$Tissue, vsd$Fragment, sep="-")
colnames(sampleDistMatrix) <- NULL
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
pheatmap(sampleDistMatrix,
clustering_distance_rows=sampleDists,
clustering_distance_cols=sampleDists,
col=colors)
pcaData <- plotPCA(vsd, intgroup=c("Tissue", "Fragment"), returnData=TRUE, ntop = 14464)
percentVar <- round(100 * attr(pcaData, "percentVar"))
PCA <- ggplot(pcaData, aes(PC1, PC2, color=Tissue, shape=Fragment)) +
geom_point(size=2) +
scale_color_manual(values = c("Aboral" = "mediumpurple1", "OralEpi" = "palegreen3"))+
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance")) +
coord_fixed() + theme_bw()
save_ggplot(PCA, "../output_RNA/differential_expression/PCA_allgenes")
PCA_small <- ggplot(pcaData, aes(PC1, PC2, color=Tissue)) +
geom_point(size=2) +
scale_color_manual(values = c("Aboral" = "mediumpurple1", "OralEpi" = "palegreen3"))+
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance")) +
coord_fixed() + theme_bw()
ggsave(filename = paste0("../output_RNA/differential_expression/PCA_allgenes_small", ".png"), plot = PCA_small, width = 4, height = 2.5, units = "in", dpi = 300)
pcaData <- plotPCA(vsd, intgroup=c("Tissue", "Fragment"), returnData=TRUE)
percentVar <- round(100 * attr(pcaData, "percentVar"))
PCA <- ggplot(pcaData, aes(PC1, PC2, color=Tissue, shape=Fragment)) +
geom_point(size=2) +
scale_color_manual(values = c("Aboral" = "mediumpurple1", "OralEpi" = "palegreen3"))+
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance")) +
coord_fixed() + theme_bw()
save_ggplot(PCA, "../output_RNA/differential_expression/PCA")
PCA_small <- ggplot(pcaData, aes(PC1, PC2, color=Tissue)) +
geom_point(size=2) +
scale_color_manual(values = c("Aboral" = "mediumpurple1", "OralEpi" = "palegreen3"))+
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance")) +
coord_fixed() + theme_bw()
ggsave(filename = paste0("../output_RNA/differential_expression/PCA_small", ".png"), plot = PCA_small, width = 4, height = 2.5, units = "in", dpi = 300)
Clearly, the majority of the variance in the data is explained by tissue type!
Download annotation files from genome website
# wget files
wget http://cyanophora.rutgers.edu/Pocillopora_acuta/Pocillopora_acuta_HIv2.genes.Conserved_Domain_Search_results.txt.gz
wget http://cyanophora.rutgers.edu/Pocillopora_acuta/Pocillopora_acuta_HIv2.genes.EggNog_results.txt.gz
wget http://cyanophora.rutgers.edu/Pocillopora_acuta/Pocillopora_acuta_HIv2.genes.KEGG_results.txt.gz
# move to references direcotry
mv *gz ../references
# unzip files
gunzip ../references/*gz
EggNog <- read.delim("../references/Pocillopora_acuta_HIv2.genes.EggNog_results.txt") %>% dplyr::rename("query" = X.query)
CDSearch <- read.delim("../references/Pocillopora_acuta_HIv2.genes.Conserved_Domain_Search_results.txt", quote = "") %>% dplyr::rename("query" = X.Query)
KEGG <- read.delim("../references/Pocillopora_acuta_HIv2.genes.KEGG_results.txt", header = FALSE) %>% dplyr::rename("query" = V1, "KeggTerm" = V2)
DE_05$query <- rownames(DE_05)
DE_05_annot <- DE_05 %>% left_join(CDSearch) %>% select(query,everything())
## Joining with `by = join_by(query)`
DE_05_eggnog <- DE_05 %>% left_join(EggNog) %>% select(query,everything())
## Joining with `by = join_by(query)`
write.csv(as.data.frame(DE_05_eggnog),
file="../output_RNA/differential_expression/DE_05_eggnog_annotation.csv")
annot_all <- as.data.frame(rownames(dds)) %>% dplyr::rename("query" = `rownames(dds)`) %>% left_join(CDSearch)
## Joining with `by = join_by(query)`
eggnog_all <- as.data.frame(rownames(dds)) %>% dplyr::rename("query" = `rownames(dds)`) %>% left_join(EggNog)
## Joining with `by = join_by(query)`
df <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])
gene_labels <- eggnog_all %>% select(query,PFAMs) %>%
mutate_all(~ ifelse(is.na(.), "", .)) %>% #replace NAs with "" for labelling purposes
separate(PFAMs, into = c("PFAMs", "rest of name"), sep = ",(?=.*?,)", extra = "merge")
## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 12723 rows [1, 2, 3, 4,
## 5, 7, 8, 9, 11, 12, 13, 14, 15, 16, 19, 20, 21, 23, 24, 25, ...].
#view most significantly differentially expressed genes
select <- order(res$padj)[1:50]
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row =gene_labels[select,"PFAMs"], fontsize_row = 5)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/top50_DE")
#view genes Higher in Aboral, Lower in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange,decreasing = TRUE)[1:50]
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_Aboral <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row =gene_labels[select,"PFAMs"], fontsize_row = 5)
up_Aboral
save_ggplot(up_Aboral, "../output_RNA/differential_expression/up_Aboral")
#view genes Lower in Aboral, Higher in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange)[1:50]
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_OralEpi <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row =gene_labels[select,"PFAMs"], fontsize_row = 5)
up_OralEpi
save_ggplot(up_OralEpi, "../output_RNA/differential_expression/up_OralEpi")
MarkerGenes <- read.csv("../references/Pacuta_MarkerGenes_Levy2021.csv") %>% dplyr::rename("query" = 1, "List" = 2, "definition" = 3) %>% filter(List !="Toolkit")
MarkerGenes_broc <- read.csv("../output_RNA/marker_genes/Pacuta_Spis_Markers_pairs.csv") %>% select(protein_id_spB,cluster,Standardized_Name_spA ) %>% dplyr::rename("query" = 1, "List" = 2)
MarkerGenes$def_short <- ifelse(nchar(MarkerGenes$definition) > 20,
paste0(substr(MarkerGenes$definition, 1, 17), "..."),
MarkerGenes$definition)
HoxGenes_Nvec <- read.csv("../output_RNA/marker_genes/Hox_nematostella.csv") %>% dplyr::rename("query" = Pacuta_gene) %>% select(-c(X))
HoxGenes_Nvec$def_short <- gsub("Homeobox protein", "Hox", HoxGenes_Nvec$Description)
HoxGenes_Nvec$def_short <- gsub("homeobox protein", "Hox", HoxGenes_Nvec$def_short)
He_etal_Nvec <- read.csv("../output_RNA/marker_genes/He_etal_nematostella.csv") %>% dplyr::rename("query" = Pacuta_gene) %>% select(-c(X))
He_etal_Nvec$def_short <- gsub("Homeobox protein", "Hox", He_etal_Nvec$Description)
He_etal_Nvec$def_short <- gsub("homeobox protein", "Hox", He_etal_Nvec$def_short)
DuBuc_etal_Nvec <- read.csv("../output_RNA/marker_genes/Wnt_nematostella.csv") %>% dplyr::rename("query" = Pacuta_gene) %>% select(-c(X))
Biomin <- read.csv("../output_RNA/marker_genes/Pacuta_Biomin.csv") %>% dplyr::rename("query" = Pocillopora_acuta_best_hit) %>% select(-c(accessionnumber.geneID, Ref))
Biomin_broc <- read.csv("../output_RNA/marker_genes/Pacuta_Biomin_Spis_ortholog.csv") %>% dplyr::rename("query" = Pacuta_gene) %>% select(-c(X,accessionnumber_gene_id, ref))
Biomin <- Biomin %>%
group_by(query,List) %>%
summarize(definition = paste(unique(definition), collapse = ","))
## `summarise()` has grouped output by 'query'. You can override using the
## `.groups` argument.
Biomin$def_short <- ifelse(nchar(Biomin$definition) > 40,
paste0(substr(Biomin$definition, 1, 37), "..."),
Biomin$definition)
Biomin_filtered_counts <- filtered_counts[(rownames(filtered_counts) %in% Biomin$query),]
Biomin_broc <- Biomin_broc %>%
group_by(query,List) %>%
summarize(definition = paste(unique(definition), collapse = ","))
## `summarise()` has grouped output by 'query'. You can override using the
## `.groups` argument.
Biomin_broc$def_short <- ifelse(nchar(Biomin_broc$definition) > 40,
paste0(substr(Biomin_broc$definition, 1, 37), "..."),
Biomin_broc$definition)
Biomin_broc_filtered_counts <- filtered_counts[(rownames(filtered_counts) %in% Biomin_broc$query),]
write.csv(Biomin_filtered_counts, "../output_RNA/differential_expression/Biomin_filtered_counts.csv")
DE_05$query <- rownames(DE_05)
resOrdered$query <- rownames(resOrdered)
DE_05_biomin <- DE_05 %>% left_join(Biomin) %>% select(query,everything()) %>% drop_na()
## Joining with `by = join_by(query)`
DESeq_biomin <- as.data.frame(resOrdered) %>% left_join(Biomin) %>% select(query,everything()) %>% drop_na()
## Joining with `by = join_by(query)`
DE_05_Biomin_broc <- DE_05 %>% left_join(Biomin_broc) %>% select(query,everything()) %>% drop_na()
## Joining with `by = join_by(query)`
DESeq_Biomin_broc <- as.data.frame(resOrdered) %>% left_join(Biomin_broc) %>% select(query,everything()) %>% drop_na()
## Joining with `by = join_by(query)`
DE_05_marker <- DE_05 %>% left_join(MarkerGenes) %>% select(query,everything()) %>% drop_na()
## Joining with `by = join_by(query)`
DE_05_marker_broc <- DE_05 %>% left_join(MarkerGenes_broc) %>% select(query,everything()) %>% drop_na()
## Joining with `by = join_by(query)`
DE_05_Hox <- DE_05 %>% left_join(HoxGenes_Nvec) %>% select(query,everything()) %>% drop_na()
## Joining with `by = join_by(query)`
DE_05_He_etal <- DE_05 %>% left_join(He_etal_Nvec) %>% select(query,everything()) %>% drop_na()
## Joining with `by = join_by(query)`
DE_05_DuBuc_etal <- DE_05 %>% left_join(DuBuc_etal_Nvec) %>% select(query,everything()) %>% drop_na()
## Joining with `by = join_by(query)`
write.csv(as.data.frame(DE_05_biomin),
file="../output_RNA/differential_expression/DE_05_biomin_annotation.csv")
write.csv(as.data.frame(DE_05_marker),
file="../output_RNA/differential_expression/DE_05_markergene_annotation.csv")
biomin_all_counts <- as.data.frame(counts(dds)) %>% mutate(query = rownames(dds)) %>% select(query,everything()) %>% left_join(Biomin)
## Joining with `by = join_by(query)`
biomin_all_res <- as.data.frame(resLFC) %>% mutate(query = rownames(resLFC)) %>% select(query,everything()) %>% left_join(Biomin)
## Joining with `by = join_by(query)`
Biomin_broc_all_counts <- as.data.frame(counts(dds)) %>% mutate(query = rownames(dds)) %>% select(query,everything()) %>% left_join(Biomin_broc)
## Joining with `by = join_by(query)`
Biomin_broc_all_res <- as.data.frame(resLFC) %>% mutate(query = rownames(resLFC)) %>% select(query,everything()) %>% left_join(Biomin_broc)
## Joining with `by = join_by(query)`
markers_all_counts <- as.data.frame(counts(dds)) %>% mutate(query = rownames(dds)) %>% select(query,everything()) %>% left_join(MarkerGenes)
## Joining with `by = join_by(query)`
markers_all_res <- as.data.frame(resLFC) %>% mutate(query = rownames(resLFC)) %>% select(query,everything()) %>% left_join(MarkerGenes)
## Joining with `by = join_by(query)`
broc_markers_all_counts <- as.data.frame(counts(dds)) %>% mutate(query = rownames(dds)) %>% select(query,everything()) %>% left_join(MarkerGenes_broc)
## Joining with `by = join_by(query)`
broc_markers_all_res <- as.data.frame(resLFC) %>% mutate(query = rownames(resLFC)) %>% select(query,everything()) %>% left_join(MarkerGenes_broc)
## Joining with `by = join_by(query)`
Hox_all_res <- as.data.frame(resLFC) %>% mutate(query = rownames(resLFC)) %>% select(query,everything()) %>% left_join(HoxGenes_Nvec)
## Joining with `by = join_by(query)`
Hox_all_res %>% drop_na()
## query baseMean log2FoldChange
## 1 Pocillopora_acuta_HIv2___TS.g15143.t1 521.8679 0.1422022
## 2 Pocillopora_acuta_HIv2___RNAseq.g12537.t1 271.6692 0.1068577
## 3 Pocillopora_acuta_HIv2___RNAseq.g24142.t1 677.5990 -0.2872155
## 4 Pocillopora_acuta_HIv2___RNAseq.g614.t1 2049.6550 3.4581160
## 5 Pocillopora_acuta_HIv2___RNAseq.18045_t 1074.8238 0.8607645
## 6 Pocillopora_acuta_HIv2___RNAseq.g3322.t1 1641.0298 -12.6389456
## 7 Pocillopora_acuta_HIv2___TS.g25076.t1 2172.4212 0.3659500
## 8 Pocillopora_acuta_HIv2___RNAseq.g1763.t1 844.6168 3.1839530
## 9 Pocillopora_acuta_HIv2___RNAseq.g9587.t1 1162.9301 1.5381041
## 10 Pocillopora_acuta_HIv2___RNAseq.g9588.t1 10129.8808 13.0019191
## 11 Pocillopora_acuta_HIv2___RNAseq.g26215.t1 665.0359 0.4339101
## 12 Pocillopora_acuta_HIv2___RNAseq.g26215.t1 665.0359 0.4339101
## lfcSE pvalue padj Gene_Name
## 1 1.0156290 1.403026e-02 5.531033e-02 B-H1
## 2 1.0129642 1.620554e-02 6.222378e-02 B-H1
## 3 0.9977356 1.453626e-01 3.413047e-01 Anthox1
## 4 1.8621899 1.683827e-04 1.284540e-03 Six4/5
## 5 1.4145958 5.029165e-03 2.338214e-02 Nkx2.2a1
## 6 3.2203985 3.690243e-11 2.629343e-09 Dlx
## 7 0.6635312 4.480396e-01 6.901301e-01 Anthox6a
## 8 1.7839837 2.174546e-04 1.603908e-03 Alx4
## 9 1.6584118 1.648080e-02 6.316330e-02 OtxC
## 10 2.9159022 2.001347e-17 1.929832e-14 OtxB
## 11 1.1064758 5.857990e-02 1.759343e-01 Anthox8a
## 12 1.1064758 5.857990e-02 1.759343e-01 Anthox8b
## Description def_short
## 1 Homeobox protein B-H1 Hox B-H1
## 2 Homeobox protein B-H1 Hox B-H1
## 3 Homeobox protein Anthox1 Hox Anthox1
## 4 Homeobox protein SIX4/5 Hox SIX4/5
## 5 Homeobox protein Nkx-2.2a Hox Nkx-2.2a
## 6 Homeobox protein Dlx Hox Dlx
## 7 Homeobox protein Anthox6a Hox Anthox6a
## 8 Homeobox protein aristaless-like 4 Hox aristaless-like 4
## 9 Homeobox protein OTX1 Hox OTX1
## 10 Homeobox protein OTX1 B Hox OTX1 B
## 11 Homeobox protein Anthox8a Hox Anthox8a
## 12 Homeobox protein Anthox8b Hox Anthox8b
df <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])
#view biomin genes that are differntially expressed
z_scores <- t(scale(t(assay(vsd)[DE_05_biomin$query, ]), center = TRUE, scale = TRUE))
DE_biomin <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = DE_05_biomin$def_short, fontsize_row = 5)
DE_biomin
save_ggplot(DE_biomin, "../output_RNA/differential_expression/DE_biomin")
#view biomin genes that are differntially expressed
z_scores <- t(scale(t(assay(vsd)[DE_05_Biomin_broc$query, ]), center = TRUE, scale = TRUE))
DE_Biomin_broc <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = DE_05_Biomin_broc$def_short, fontsize_row = 5)
DE_Biomin_broc
save_ggplot(DE_Biomin_broc, "../output_RNA/differential_expression/DE_Biomin_broc")
#view marker genes that are differntially expressed
z_scores <- t(scale(t(assay(vsd)[DE_05_marker$query, ]), center = TRUE, scale = TRUE))
DE_marker <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,cutree_rows = 5,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = DE_05_marker$List, fontsize_row = 4)
DE_marker
save_ggplot(DE_marker, "../output_RNA/differential_expression/DE_marker")
DE_05_marker_grouped <- DE_05_marker %>% arrange(List) %>% mutate(List = as.factor(List))
z_scores <- t(scale(t(assay(vsd)[DE_05_marker_grouped$quer, ]), center = TRUE, scale = TRUE))
DE_05_marker_grouped_plot <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = DE_05_marker_grouped$List, fontsize_row = 5)
DE_05_marker_grouped_plot
save_ggplot(DE_05_marker_grouped_plot, "../output_RNA/differential_expression/DE_05_marker_grouped")
#view marker genes that are differntially expressed
z_scores <- t(scale(t(assay(vsd)[DE_05_marker_broc$query, ]), center = TRUE, scale = TRUE))
DE_marker <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,cutree_rows = 5,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = DE_05_marker_broc$List, fontsize_row = 4)
DE_marker
save_ggplot(DE_marker, "../output_RNA/differential_expression/DE_marker_broc")
DE_05_marker_broc_grouped <- DE_05_marker_broc %>% arrange(List) %>% mutate(List = as.factor(List))
z_scores <- t(scale(t(assay(vsd)[DE_05_marker_broc_grouped$quer, ]), center = TRUE, scale = TRUE))
DE_05_marker_broc_grouped_plot <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = DE_05_marker_broc_grouped$List, fontsize_row = 5)
DE_05_marker_broc_grouped_plot
save_ggplot(DE_05_marker_broc_grouped_plot, "../output_RNA/differential_expression/DE_05_marker_broc_grouped")
Biomin_volcano <- EnhancedVolcano(biomin_all_res,
lab = biomin_all_res$def_short,
x = 'log2FoldChange',
y = 'padj',
pCutoff = 0.01,
drawConnectors = TRUE,
widthConnectors = 0.75,
pointSize = 1,
labSize = 2,boxedLabels = TRUE,max.overlaps = 40)
save_ggplot(Biomin_volcano, "../output_RNA/differential_expression/Biomin_volcano")
Biomin_broc_volcano <- EnhancedVolcano(Biomin_broc_all_res,
lab = Biomin_broc_all_res$def_short,
x = 'log2FoldChange',
y = 'padj',
pCutoff = 0.01,
drawConnectors = TRUE,
widthConnectors = 0.75,
pointSize = 1,
labSize = 2,boxedLabels = TRUE,max.overlaps = 40)
save_ggplot(Biomin_broc_volcano, "../output_RNA/differential_expression/Biomin_broc_volcano")
Marker_volcano <- EnhancedVolcano(markers_all_res,
lab = markers_all_res$List,
x = 'log2FoldChange',
y = 'padj',
pCutoff = 0.01,
drawConnectors = TRUE,
widthConnectors = 0.75,
pointSize = 1,
labSize = 2,boxedLabels = TRUE,max.overlaps = 60)
save_ggplot(Marker_volcano, "../output_RNA/differential_expression/Marker_volcano")
Marker_volcano_names <- EnhancedVolcano(markers_all_res,
lab = markers_all_res$def_short,
x = 'log2FoldChange',
y = 'padj',
pCutoff = 0.01,
drawConnectors = TRUE,
widthConnectors = 0.75,
pointSize = 1,
labSize = 2,boxedLabels = TRUE,max.overlaps = 60)
save_ggplot(Marker_volcano_names, "../output_RNA/differential_expression/Marker_volcano_names")
Marker_volcano <- EnhancedVolcano(broc_markers_all_res,
lab = broc_markers_all_res$List,
x = 'log2FoldChange',
y = 'padj',
pCutoff = 0.01,
drawConnectors = TRUE,
widthConnectors = 0.75,
pointSize = 1,
labSize = 2,boxedLabels = TRUE,max.overlaps = 60)
save_ggplot(Marker_volcano, "../output_RNA/differential_expression/Marker_volcano_broc")
EnhancedVolcano(resLFC,
lab = rownames(resLFC),
x = 'log2FoldChange',
y = 'pvalue')
volcano_plain <- EnhancedVolcano(resLFC,
lab = NA,
x = 'log2FoldChange',
y = 'padj',
pCutoff = 0.05,
title="",
subtitle="",
drawConnectors = TRUE,
widthConnectors = 0.75,
pointSize = 1,
labSize = 2,boxedLabels = TRUE,max.overlaps = 60)
save_ggplot(volcano_plain, "../output_RNA/differential_expression/volcano_plain",width = 4, height = 6, units = "in", dpi = 300)
save_ggplot(volcano_plain, "../output_RNA/differential_expression/volcano_plain")
# wget protein sequence reference file
wget http://cyanophora.rutgers.edu/Pocillopora_acuta/Pocillopora_acuta_HIv2.genes.pep.faa.gz
# move to references direcotry
mv *gz ../references
# unzip files
gunzip ../references/*gz
#get the names of all the DEGs from the first column of the DEG csv file
tail -n +2 ../output_RNA/differential_expression/DEG_05.csv | cut -d',' -f1 | tr -d '"' > ../output_RNA/differential_expression/DEG_05_names.csv
#grep this file against the protein fasta file, first with wc -l to make sure the number of lines is correct (should be your number of DEGs)
grep -f ../output_RNA/differential_expression/DEG_05_names.csv ../references/Pocillopora_acuta_HIv2.genes.pep.faa | wc -l
#grep each header with the protein sequence after ("-A 1") and save to a new file
grep -A 1 -f ../output_RNA/differential_expression/DEG_05_names.csv ../references/Pocillopora_acuta_HIv2.genes.pep.faa > ../output_RNA/differential_expression/DEG_05_seqs.txt
On andromeda:
Blastp-ing only the DE genes against the entire nr database (will take a while)
cd ../scripts
nano DEG_05_blast.sh
#!/bin/bash
#SBATCH --job-name="DE_blast"
#SBATCH -t 240:00:00
#SBATCH --export=NONE
#SBATCH --mail-type=BEGIN,END,FAIL #email you when job starts, stops and/or fails
#SBATCH --mail-user=zdellaert@uri.edu #your email to send notifications
#SBATCH --mem=500GB
#SBATCH --error=../scripts/outs_errs/"%x_error.%j" #write out slurm error reports
#SBATCH --output=../scripts/outs_errs/"%x_output.%j" #write out any program outpus
#SBATCH --account=putnamlab
#SBATCH --nodes=2 --ntasks-per-node=24
module load BLAST+/2.15.0-gompi-2023a
cd ../output_RNA/differential_expression #set working directory
mkdir blast
cd blast
#nr database location andromeda: /data/shared/ncbi-db/.ncbirc
# points to current location: cat /data/shared/ncbi-db/.ncbirc
# [BLAST]
# BLASTDB=/data/shared/ncbi-db/2024-11-10
blastp -query ../DEG_05_seqs.txt -db nr -out DEG_05_blast_results.txt -outfmt 0 -evalue 1E-05 \
-num_threads 48 \
-max_target_seqs 10
blastp -query ../DEG_05_seqs.txt -db nr -out DEG_05_blast_results_tab.txt -outfmt 6 -evalue 1E-05 \
-num_threads 48 \
-max_target_seqs 1 \
-max_hsps 1
echo "Blast complete!" $(date)
sbatch DEG_05_blast.sh
cd ../output_RNA/differential_expression/blast
wc -l DEG_05_blast_results_tab.txt #3537 of the 3606 genes were annotated
#get just the NCBI database accession numbers for the blast results
cut -f2 DEG_05_blast_results_tab.txt > DEG_05_blast_accessions.txt
#remove any duplicates
sort -u DEG_05_blast_accessions.txt > unique_DEG_05_blast_accessions.txt
wc -l unique_DEG_05_blast_accessions.txt #3404 of the 3537 annotations were unique
while read acc; do
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=protein&id=$acc&rettype=gp&retmode=text" \
| grep "DEFINITION" | sed 's/DEFINITION //g' | awk -v id="$acc" '{print id "\t" $0}'
done < unique_DEG_05_blast_accessions.txt > DEG_05_blast_names.txt
wc -l DEG_05_blast_names.txt #3396 ; unsure why 8 are missing.
join -1 2 -2 1 -t $'\t' <(sort -k2 DEG_05_blast_results_tab.txt) <(sort DEG_05_blast_names.txt) > annotated_DEG_05_blast_results_tab.txt
On unity:
swissprot based on https://github.com/urol-e5/deep-dive/blob/main/D-Apul/code/20-Apul-gene-annotation.Rmd and https://github.com/urol-e5/deep-dive/blob/main/F-Pmea/code/20-Pmea-gene-annotation.Rmd
Steven’s notebook post here: https://sr320.github.io/tumbling-oysters/posts/sr320-27-go/
mkdir ../references/blast_dbs
cd ../references/blast_dbs
curl -O https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.fasta.gz
mv uniprot_sprot.fasta.gz uniprot_sprot_r2024_10_02.fasta.gz
gunzip -k uniprot_sprot_r2024_10_02.fasta.gz
rm uniprot_sprot_r2024_10_02.fasta.gz
head uniprot_sprot_r2024_10_02.fasta
echo "Number of Sequences"
grep -c ">" uniprot_sprot_r2024_10_02.fasta
# 572214 sequences
module load blast-plus/2.14.1
makeblastdb \
-in ../references/blast_dbs/uniprot_sprot_r2024_10_02.fasta \
-dbtype prot \
-out ../references/blast_dbs/uniprot_sprot_r2024_10_02
cd ../scripts
nano blastp_SwissProt.sh
#!/bin/bash
#SBATCH -t 18:00:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=48
#SBATCH --mem=500GB
#SBATCH --export=NONE
#SBATCH --error=../scripts/outs_errs/"%x_error.%j" #write out slurm error reports
#SBATCH --output=../scripts/outs_errs/"%x_output.%j" #write out any program outpus
#SBATCH --mail-type=BEGIN,END,FAIL #email you when job starts, stops and/or fails
#SBATCH -D /project/pi_hputnam_uri_edu/zdellaert/LaserCoral #set working directory
module load blast-plus/2.14.1
cd references/
mkdir annotation
fasta="Pocillopora_acuta_HIv2.genes.pep.faa"
blastp \
-query $fasta \
-db blast_dbs/uniprot_sprot_r2024_10_02 \
-out annotation/blastp_SwissProt_out.tab \
-evalue 1E-05 \
-num_threads 48 \
-max_target_seqs 1 \
-max_hsps 1 \
-outfmt 6
echo "Blast complete!" $(date)
cd references/annotation/
tr '|' '\t' < blastp_SwissProt_out.tab > blastp_SwissProt_out_sep.tab
cd ../references/annotation/
curl -H "Accept: text/plain; format=tsv" "https://rest.uniprot.org/uniprotkb/stream?fields=accession%2Creviewed%2Cid%2Cprotein_name%2Cgene_names%2Corganism_name%2Clength%2Cgo_p%2Cgo%2Cgo_id%2Cgo_c%2Cgo_f&format=tsv&query=%28reviewed%3Atrue%29" -o SwissProt-Annot-GO_111524.tsv
wc -l SwissProt-Annot-GO_111524.tsv
#572215
All code below based on https://github.com/urol-e5/deep-dive/blob/main/D-Apul/code/20-Apul-gene-annotation.Rmd and https://github.com/urol-e5/deep-dive/blob/main/F-Pmea/code/20-Pmea-gene-annotation.Rmd
Steven’s notebook post here: https://sr320.github.io/tumbling-oysters/posts/sr320-27-go/
bltabl <- read.csv("../references/annotation/blastp_SwissProt_out_sep.tab", sep = '\t', header = FALSE)
spgo <- read.csv("../references/annotation/SwissProt-Annot-GO_111524.tsv", sep = '\t', header = TRUE)
annot_tab <- left_join(bltabl, spgo, by = c("V3" = "Entry")) %>%
select(
query = V1,
blast_hit = V3,
evalue = V13,
ProteinNames = Protein.names,
BiologicalProcess = Gene.Ontology..biological.process.,
GeneOntologyIDs = Gene.Ontology.IDs
)
head(annot_tab)
## query blast_hit evalue
## 1 Pocillopora_acuta_HIv2___RNAseq.g24143.t1a Q4JAI4 1.02e-37
## 2 Pocillopora_acuta_HIv2___RNAseq.g18333.t1 O08807 9.62e-116
## 3 Pocillopora_acuta_HIv2___RNAseq.g7985.t1 O74212 3.56e-158
## 4 Pocillopora_acuta_HIv2___TS.g15308.t1 Q09575 1.08e-12
## 5 Pocillopora_acuta_HIv2___RNAseq.g2057.t1 P0C1P0 8.81e-14
## 6 Pocillopora_acuta_HIv2___RNAseq.g4696.t1 Q9W2Q5 8.98e-69
## ProteinNames
## 1 Methionine synthase (EC 2.1.1.-) (Homocysteine methyltransferase)
## 2 Peroxiredoxin-4 (EC 1.11.1.24) (Antioxidant enzyme AOE372) (Peroxiredoxin IV) (Prx-IV) (Thioredoxin peroxidase AO372) (Thioredoxin-dependent peroxide reductase A0372) (Thioredoxin-dependent peroxiredoxin 4)
## 3 Acyl-lipid (8-3)-desaturase (EC 1.14.19.30) (Delta(5) fatty acid desaturase) (Delta-5 fatty acid desaturase)
## 4 Uncharacterized protein K02A2.6
## 5 Phosphatidylinositol N-acetylglucosaminyltransferase subunit Y (Phosphatidylinositol-glycan biosynthesis class Y protein) (PIG-Y)
## 6 Calcium and integrin-binding family member 2
## BiologicalProcess
## 1 methionine biosynthetic process [GO:0009086]; methylation [GO:0032259]
## 2 cell redox homeostasis [GO:0045454]; extracellular matrix organization [GO:0030198]; hydrogen peroxide catabolic process [GO:0042744]; male gonad development [GO:0008584]; negative regulation of male germ cell proliferation [GO:2000255]; protein maturation by protein folding [GO:0022417]; reactive oxygen species metabolic process [GO:0072593]; response to oxidative stress [GO:0006979]; spermatogenesis [GO:0007283]
## 3 long-chain fatty acid biosynthetic process [GO:0042759]; unsaturated fatty acid biosynthetic process [GO:0006636]
## 4 DNA integration [GO:0015074]
## 5 GPI anchor biosynthetic process [GO:0006506]
## 6 calcium ion homeostasis [GO:0055074]; phototransduction [GO:0007602]
## GeneOntologyIDs
## 1 GO:0003871; GO:0008270; GO:0009086; GO:0032259
## 2 GO:0005615; GO:0005737; GO:0005739; GO:0005783; GO:0005790; GO:0005829; GO:0006979; GO:0007283; GO:0008379; GO:0008584; GO:0022417; GO:0030198; GO:0042744; GO:0042802; GO:0045454; GO:0072593; GO:0140313; GO:2000255
## 3 GO:0006636; GO:0016020; GO:0020037; GO:0042759; GO:0046872; GO:0102866
## 4 GO:0003676; GO:0005737; GO:0008270; GO:0015074; GO:0019899; GO:0042575
## 5 GO:0000506; GO:0006506
## 6 GO:0000287; GO:0005509; GO:0005737; GO:0007602; GO:0055074
write.table(annot_tab,
file = "../references/annotation/protein-GO.tsv",
sep = "\t",
row.names = FALSE,
quote = FALSE)
DE_05_SwissProt <- DE_05 %>% left_join(annot_tab) %>% select(query,everything())
## Joining with `by = join_by(query)`
write.csv(as.data.frame(DE_05_SwissProt),
file="../output_RNA/differential_expression/DE_05_SwissProt_annotation.csv")
df <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])
DE_05_SwissProt$short_name <- ifelse(nchar(DE_05_SwissProt$ProteinNames) > 30,
paste0(substr(DE_05_SwissProt$ProteinNames, 1, 27), "..."),
DE_05_SwissProt$ProteinNames)
gene_labels <- DE_05_SwissProt %>%
select(query,short_name) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
#view most significantly differentially expressed genes
select <- order(res$padj)[1:50]
rownames(res)[select]
## [1] "Pocillopora_acuta_HIv2___RNAseq.g6351.t1"
## [2] "Pocillopora_acuta_HIv2___RNAseq.g28575.t1a"
## [3] "Pocillopora_acuta_HIv2___RNAseq.g27038.t1"
## [4] "Pocillopora_acuta_HIv2___RNAseq.g2165.t1"
## [5] "Pocillopora_acuta_HIv2___TS.g18104.t1"
## [6] "Pocillopora_acuta_HIv2___RNAseq.g14330.t3"
## [7] "Pocillopora_acuta_HIv2___RNAseq.g14025.t1"
## [8] "Pocillopora_acuta_HIv2___RNAseq.g14090.t1"
## [9] "Pocillopora_acuta_HIv2___RNAseq.g10378.t1"
## [10] "Pocillopora_acuta_HIv2___RNAseq.g3832.t1"
## [11] "Pocillopora_acuta_HIv2___RNAseq.g8006.t1"
## [12] "Pocillopora_acuta_HIv2___RNAseq.g14253.t1"
## [13] "Pocillopora_acuta_HIv2___RNAseq.g25431.t1"
## [14] "Pocillopora_acuta_HIv2___RNAseq.11056_t"
## [15] "Pocillopora_acuta_HIv2___RNAseq.g9588.t1"
## [16] "Pocillopora_acuta_HIv2___TS.g30765.t1"
## [17] "Pocillopora_acuta_HIv2___RNAseq.g14021.t1"
## [18] "Pocillopora_acuta_HIv2___RNAseq.g19082.t1"
## [19] "Pocillopora_acuta_HIv2___RNAseq.g16202.t1"
## [20] "Pocillopora_acuta_HIv2___TS.g19991.t2"
## [21] "Pocillopora_acuta_HIv2___RNAseq.g20860.t1"
## [22] "Pocillopora_acuta_HIv2___RNAseq.g8588.t1"
## [23] "Pocillopora_acuta_HIv2___RNAseq.g26604.t1"
## [24] "Pocillopora_acuta_HIv2___TS.g18100.t1"
## [25] "Pocillopora_acuta_HIv2___TS.g27642.t1b"
## [26] "Pocillopora_acuta_HIv2___RNAseq.g11588.t1"
## [27] "Pocillopora_acuta_HIv2___RNAseq.g25327.t1"
## [28] "Pocillopora_acuta_HIv2___RNAseq.g12281.t1"
## [29] "Pocillopora_acuta_HIv2___RNAseq.g19085.t1"
## [30] "Pocillopora_acuta_HIv2___RNAseq.g19284.t1"
## [31] "Pocillopora_acuta_HIv2___TS.g11360.t1"
## [32] "Pocillopora_acuta_HIv2___RNAseq.g7803.t1"
## [33] "Pocillopora_acuta_HIv2___TS.g16008.t2"
## [34] "Pocillopora_acuta_HIv2___RNAseq.g22978.t3b"
## [35] "Pocillopora_acuta_HIv2___RNAseq.g2406.t1"
## [36] "Pocillopora_acuta_HIv2___RNAseq.g8119.t1"
## [37] "Pocillopora_acuta_HIv2___TS.g9414.t1"
## [38] "Pocillopora_acuta_HIv2___RNAseq.g21374.t1"
## [39] "Pocillopora_acuta_HIv2___RNAseq.g21000.t1"
## [40] "Pocillopora_acuta_HIv2___RNAseq.g5252.t1"
## [41] "Pocillopora_acuta_HIv2___RNAseq.g22853.t1"
## [42] "Pocillopora_acuta_HIv2___RNAseq.g17117.t1"
## [43] "Pocillopora_acuta_HIv2___RNAseq.g25759.t1"
## [44] "Pocillopora_acuta_HIv2___TS.g16802.t1"
## [45] "Pocillopora_acuta_HIv2___RNAseq.g24120.t1"
## [46] "Pocillopora_acuta_HIv2___RNAseq.30415_t"
## [47] "Pocillopora_acuta_HIv2___RNAseq.g14336.t1"
## [48] "Pocillopora_acuta_HIv2___RNAseq.g26594.t2"
## [49] "Pocillopora_acuta_HIv2___RNAseq.g19115.t1"
## [50] "Pocillopora_acuta_HIv2___TS.g4983.t1"
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 6)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/top50_DE_SwissProt")
#view most significantly differentially expressed genes by LFC
select <- order(abs(res$log2FoldChange),decreasing = TRUE)[1:50]
rownames(res)[select]
## [1] "Pocillopora_acuta_HIv2___RNAseq.g28575.t1a"
## [2] "Pocillopora_acuta_HIv2___TS.g18104.t1"
## [3] "Pocillopora_acuta_HIv2___RNAseq.g27038.t1"
## [4] "Pocillopora_acuta_HIv2___RNAseq.g14025.t1"
## [5] "Pocillopora_acuta_HIv2___RNAseq.g2165.t1"
## [6] "Pocillopora_acuta_HIv2___RNAseq.g14330.t3"
## [7] "Pocillopora_acuta_HIv2___RNAseq.g14090.t1"
## [8] "Pocillopora_acuta_HIv2___TS.g30765.t1"
## [9] "Pocillopora_acuta_HIv2___RNAseq.g8006.t1"
## [10] "Pocillopora_acuta_HIv2___RNAseq.g10378.t1"
## [11] "Pocillopora_acuta_HIv2___RNAseq.g14253.t1"
## [12] "Pocillopora_acuta_HIv2___RNAseq.11056_t"
## [13] "Pocillopora_acuta_HIv2___RNAseq.g22261.t1"
## [14] "Pocillopora_acuta_HIv2___RNAseq.g14021.t1"
## [15] "Pocillopora_acuta_HIv2___RNAseq.g19082.t1"
## [16] "Pocillopora_acuta_HIv2___TS.g19991.t2"
## [17] "Pocillopora_acuta_HIv2___RNAseq.g19284.t1"
## [18] "Pocillopora_acuta_HIv2___TS.g4983.t1"
## [19] "Pocillopora_acuta_HIv2___RNAseq.g21000.t1"
## [20] "Pocillopora_acuta_HIv2___RNAseq.g20860.t1"
## [21] "Pocillopora_acuta_HIv2___TS.g9414.t1"
## [22] "Pocillopora_acuta_HIv2___RNAseq.g8588.t1"
## [23] "Pocillopora_acuta_HIv2___RNAseq.g11588.t1"
## [24] "Pocillopora_acuta_HIv2___RNAseq.g12281.t1"
## [25] "Pocillopora_acuta_HIv2___TS.g27642.t1b"
## [26] "Pocillopora_acuta_HIv2___RNAseq.g7803.t1"
## [27] "Pocillopora_acuta_HIv2___RNAseq.g8119.t1"
## [28] "Pocillopora_acuta_HIv2___RNAseq.g17117.t1"
## [29] "Pocillopora_acuta_HIv2___RNAseq.g22978.t3b"
## [30] "Pocillopora_acuta_HIv2___RNAseq.30415_t"
## [31] "Pocillopora_acuta_HIv2___TS.g16008.t2"
## [32] "Pocillopora_acuta_HIv2___RNAseq.g2406.t1"
## [33] "Pocillopora_acuta_HIv2___RNAseq.g25759.t1"
## [34] "Pocillopora_acuta_HIv2___RNAseq.g24120.t1"
## [35] "Pocillopora_acuta_HIv2___RNAseq.g14336.t1"
## [36] "Pocillopora_acuta_HIv2___RNAseq.g9631.t1"
## [37] "Pocillopora_acuta_HIv2___RNAseq.g7627.t1"
## [38] "Pocillopora_acuta_HIv2___RNAseq.g8062.t1"
## [39] "Pocillopora_acuta_HIv2___RNAseq.g1102.t1"
## [40] "Pocillopora_acuta_HIv2___RNAseq.g14484.t1"
## [41] "Pocillopora_acuta_HIv2___RNAseq.g21373.t1"
## [42] "Pocillopora_acuta_HIv2___TS.g16384.t1"
## [43] "Pocillopora_acuta_HIv2___RNAseq.g13561.t1"
## [44] "Pocillopora_acuta_HIv2___RNAseq.g1126.t1"
## [45] "Pocillopora_acuta_HIv2___RNAseq.g9210.t1"
## [46] "Pocillopora_acuta_HIv2___RNAseq.g24649.t1"
## [47] "Pocillopora_acuta_HIv2___RNAseq.g11659.t1"
## [48] "Pocillopora_acuta_HIv2___TS.g25577.t1a"
## [49] "Pocillopora_acuta_HIv2___RNAseq.g27681.t1b"
## [50] "Pocillopora_acuta_HIv2___TS.g1968.t2"
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 6)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/top50_LFC_DE_SwissProt")
#view genes Higher in Aboral, Lower in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange,decreasing = TRUE)[1:50]
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_Aboral <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 5)
up_Aboral
save_ggplot(up_Aboral, "../output_RNA/differential_expression/up_Aboral_SwissProt")
#view genes Lower in Aboral, Higher in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange)[1:50]
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_OralEpi <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row =gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 5)
up_OralEpi
save_ggplot(up_OralEpi, "../output_RNA/differential_expression/up_OralEpi_SwissProt")
df <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])
DE_05_SwissProt$short_GO <- ifelse(nchar(DE_05_SwissProt$BiologicalProcess) > 30,
paste0(substr(DE_05_SwissProt$BiologicalProcess, 1, 27), "..."),
DE_05_SwissProt$BiologicalProcess)
gene_labels <- DE_05_SwissProt %>% select(query,short_GO) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
#view most significantly differentially expressed genes
select <- order(res$padj)[1:50]
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row =gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 5)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/top50_DE_Blast2GO")
#view genes Higher in Aboral, Lower in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange,decreasing = TRUE)[1:50]
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_Aboral <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row =gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 5)
up_Aboral
save_ggplot(up_Aboral, "../output_RNA/differential_expression/up_Aboral_Blast2GO")
#view genes Lower in Aboral, Higher in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange)[1:50]
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_OralEpi <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row =gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 5)
up_OralEpi
save_ggplot(up_OralEpi, "../output_RNA/differential_expression/up_OralEpi_Blast2GO")
Manual <- read.csv("../output_RNA/differential_expression/DE_05_Manual_annotation.csv") %>% dplyr::rename("query" = 2, "definition" = 3) %>% arrange(X)
df <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])
gene_labels <- Manual %>%
select(query,Heatmap_Label) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
#view most significantly differentially expressed genes
select <- order(res$padj)[1:50]
rownames(res)[select]
## [1] "Pocillopora_acuta_HIv2___RNAseq.g6351.t1"
## [2] "Pocillopora_acuta_HIv2___RNAseq.g28575.t1a"
## [3] "Pocillopora_acuta_HIv2___RNAseq.g27038.t1"
## [4] "Pocillopora_acuta_HIv2___RNAseq.g2165.t1"
## [5] "Pocillopora_acuta_HIv2___TS.g18104.t1"
## [6] "Pocillopora_acuta_HIv2___RNAseq.g14330.t3"
## [7] "Pocillopora_acuta_HIv2___RNAseq.g14025.t1"
## [8] "Pocillopora_acuta_HIv2___RNAseq.g14090.t1"
## [9] "Pocillopora_acuta_HIv2___RNAseq.g10378.t1"
## [10] "Pocillopora_acuta_HIv2___RNAseq.g3832.t1"
## [11] "Pocillopora_acuta_HIv2___RNAseq.g8006.t1"
## [12] "Pocillopora_acuta_HIv2___RNAseq.g14253.t1"
## [13] "Pocillopora_acuta_HIv2___RNAseq.g25431.t1"
## [14] "Pocillopora_acuta_HIv2___RNAseq.11056_t"
## [15] "Pocillopora_acuta_HIv2___RNAseq.g9588.t1"
## [16] "Pocillopora_acuta_HIv2___TS.g30765.t1"
## [17] "Pocillopora_acuta_HIv2___RNAseq.g14021.t1"
## [18] "Pocillopora_acuta_HIv2___RNAseq.g19082.t1"
## [19] "Pocillopora_acuta_HIv2___RNAseq.g16202.t1"
## [20] "Pocillopora_acuta_HIv2___TS.g19991.t2"
## [21] "Pocillopora_acuta_HIv2___RNAseq.g20860.t1"
## [22] "Pocillopora_acuta_HIv2___RNAseq.g8588.t1"
## [23] "Pocillopora_acuta_HIv2___RNAseq.g26604.t1"
## [24] "Pocillopora_acuta_HIv2___TS.g18100.t1"
## [25] "Pocillopora_acuta_HIv2___TS.g27642.t1b"
## [26] "Pocillopora_acuta_HIv2___RNAseq.g11588.t1"
## [27] "Pocillopora_acuta_HIv2___RNAseq.g25327.t1"
## [28] "Pocillopora_acuta_HIv2___RNAseq.g12281.t1"
## [29] "Pocillopora_acuta_HIv2___RNAseq.g19085.t1"
## [30] "Pocillopora_acuta_HIv2___RNAseq.g19284.t1"
## [31] "Pocillopora_acuta_HIv2___TS.g11360.t1"
## [32] "Pocillopora_acuta_HIv2___RNAseq.g7803.t1"
## [33] "Pocillopora_acuta_HIv2___TS.g16008.t2"
## [34] "Pocillopora_acuta_HIv2___RNAseq.g22978.t3b"
## [35] "Pocillopora_acuta_HIv2___RNAseq.g2406.t1"
## [36] "Pocillopora_acuta_HIv2___RNAseq.g8119.t1"
## [37] "Pocillopora_acuta_HIv2___TS.g9414.t1"
## [38] "Pocillopora_acuta_HIv2___RNAseq.g21374.t1"
## [39] "Pocillopora_acuta_HIv2___RNAseq.g21000.t1"
## [40] "Pocillopora_acuta_HIv2___RNAseq.g5252.t1"
## [41] "Pocillopora_acuta_HIv2___RNAseq.g22853.t1"
## [42] "Pocillopora_acuta_HIv2___RNAseq.g17117.t1"
## [43] "Pocillopora_acuta_HIv2___RNAseq.g25759.t1"
## [44] "Pocillopora_acuta_HIv2___TS.g16802.t1"
## [45] "Pocillopora_acuta_HIv2___RNAseq.g24120.t1"
## [46] "Pocillopora_acuta_HIv2___RNAseq.30415_t"
## [47] "Pocillopora_acuta_HIv2___RNAseq.g14336.t1"
## [48] "Pocillopora_acuta_HIv2___RNAseq.g26594.t2"
## [49] "Pocillopora_acuta_HIv2___RNAseq.g19115.t1"
## [50] "Pocillopora_acuta_HIv2___TS.g4983.t1"
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 6)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/top50_DE_Manual")
#view most significantly differentially expressed genes by LFC
select <- order(abs(res$log2FoldChange),decreasing = TRUE)[1:50]
rownames(res)[select]
## [1] "Pocillopora_acuta_HIv2___RNAseq.g28575.t1a"
## [2] "Pocillopora_acuta_HIv2___TS.g18104.t1"
## [3] "Pocillopora_acuta_HIv2___RNAseq.g27038.t1"
## [4] "Pocillopora_acuta_HIv2___RNAseq.g14025.t1"
## [5] "Pocillopora_acuta_HIv2___RNAseq.g2165.t1"
## [6] "Pocillopora_acuta_HIv2___RNAseq.g14330.t3"
## [7] "Pocillopora_acuta_HIv2___RNAseq.g14090.t1"
## [8] "Pocillopora_acuta_HIv2___TS.g30765.t1"
## [9] "Pocillopora_acuta_HIv2___RNAseq.g8006.t1"
## [10] "Pocillopora_acuta_HIv2___RNAseq.g10378.t1"
## [11] "Pocillopora_acuta_HIv2___RNAseq.g14253.t1"
## [12] "Pocillopora_acuta_HIv2___RNAseq.11056_t"
## [13] "Pocillopora_acuta_HIv2___RNAseq.g22261.t1"
## [14] "Pocillopora_acuta_HIv2___RNAseq.g14021.t1"
## [15] "Pocillopora_acuta_HIv2___RNAseq.g19082.t1"
## [16] "Pocillopora_acuta_HIv2___TS.g19991.t2"
## [17] "Pocillopora_acuta_HIv2___RNAseq.g19284.t1"
## [18] "Pocillopora_acuta_HIv2___TS.g4983.t1"
## [19] "Pocillopora_acuta_HIv2___RNAseq.g21000.t1"
## [20] "Pocillopora_acuta_HIv2___RNAseq.g20860.t1"
## [21] "Pocillopora_acuta_HIv2___TS.g9414.t1"
## [22] "Pocillopora_acuta_HIv2___RNAseq.g8588.t1"
## [23] "Pocillopora_acuta_HIv2___RNAseq.g11588.t1"
## [24] "Pocillopora_acuta_HIv2___RNAseq.g12281.t1"
## [25] "Pocillopora_acuta_HIv2___TS.g27642.t1b"
## [26] "Pocillopora_acuta_HIv2___RNAseq.g7803.t1"
## [27] "Pocillopora_acuta_HIv2___RNAseq.g8119.t1"
## [28] "Pocillopora_acuta_HIv2___RNAseq.g17117.t1"
## [29] "Pocillopora_acuta_HIv2___RNAseq.g22978.t3b"
## [30] "Pocillopora_acuta_HIv2___RNAseq.30415_t"
## [31] "Pocillopora_acuta_HIv2___TS.g16008.t2"
## [32] "Pocillopora_acuta_HIv2___RNAseq.g2406.t1"
## [33] "Pocillopora_acuta_HIv2___RNAseq.g25759.t1"
## [34] "Pocillopora_acuta_HIv2___RNAseq.g24120.t1"
## [35] "Pocillopora_acuta_HIv2___RNAseq.g14336.t1"
## [36] "Pocillopora_acuta_HIv2___RNAseq.g9631.t1"
## [37] "Pocillopora_acuta_HIv2___RNAseq.g7627.t1"
## [38] "Pocillopora_acuta_HIv2___RNAseq.g8062.t1"
## [39] "Pocillopora_acuta_HIv2___RNAseq.g1102.t1"
## [40] "Pocillopora_acuta_HIv2___RNAseq.g14484.t1"
## [41] "Pocillopora_acuta_HIv2___RNAseq.g21373.t1"
## [42] "Pocillopora_acuta_HIv2___TS.g16384.t1"
## [43] "Pocillopora_acuta_HIv2___RNAseq.g13561.t1"
## [44] "Pocillopora_acuta_HIv2___RNAseq.g1126.t1"
## [45] "Pocillopora_acuta_HIv2___RNAseq.g9210.t1"
## [46] "Pocillopora_acuta_HIv2___RNAseq.g24649.t1"
## [47] "Pocillopora_acuta_HIv2___RNAseq.g11659.t1"
## [48] "Pocillopora_acuta_HIv2___TS.g25577.t1a"
## [49] "Pocillopora_acuta_HIv2___RNAseq.g27681.t1b"
## [50] "Pocillopora_acuta_HIv2___TS.g1968.t2"
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 6)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/top50_LFC_DE_Manual")
#view genes Higher in Aboral, Lower in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange,decreasing = TRUE)[1:50]
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_Aboral <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), labels_col = NA, annotation_colors = ann_colors,
labels_row =gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 8.5)
up_Aboral
save_ggplot(up_Aboral, "../output_RNA/differential_expression/up_Aboral_Manual")
#view genes Lower in Aboral, Higher in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange)[1:50]
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_OralEpi <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), labels_col = NA, annotation_colors = ann_colors,
labels_row =gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 8.5)
up_OralEpi
save_ggplot(up_OralEpi, "../output_RNA/differential_expression/up_OralEpi_Manual")
cd ../references
#download the genome protein fasta if you have not already
wget http://cyanophora.rutgers.edu/Pocillopora_acuta/Pocillopora_acuta_HIv2.genes.pep.faa.gz
#unzip file
gunzip Pocillopora_acuta_HIv2.genes.pep.faa.gz
In unity
salloc -p cpu -c 8 --mem 32G
module load uri/main
module load BLAST+/2.15.0-gompi-2023a
#make blast_dbs directory if you haven't done so above
mkdir blast_dbs
cd blast_dbs
makeblastdb -in ../Pocillopora_acuta_HIv2.genes.pep.faa -out Pacuta_prot -dbtype prot
cd ../../output_RNA/differential_expression/
#make blast output directory if you haven't done so above
mkdir blast
cd blast
nano YinYang.txt
add accession numbers of interest:
XP_048585772.1
XP_048585773.1
XP_048585774.1
NP_996806.2
NP_777560.2
# Read the input file line by line and fetch FASTA sequences
while read -r accession; do
if [[ -n "$accession" ]]; then
echo "Fetching $accession..."
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=protein&id=${accession}&rettype=fasta&retmode=text" >> "YinYang.fasta"
echo >> "YinYang.fasta" # Add a newline between sequences
sleep 1 # Avoid hitting rate limits
fi
done < "YinYang.txt"
# run blast with human-readable output
blastp -query YinYang.fasta -db ../../../references/blast_dbs/Pacuta_prot -out YinYang_blast_results.txt -outfmt 0
#looks like there are a lot of matches for each gene! I am going to do a tab search with a very low e-value cutoff:
# run blast with tabular output
blastp -query YinYang.fasta -db ../../../references/blast_dbs/Pacuta_prot -out YinYang_blast_results_tab.txt -outfmt 6 -evalue 1e-25
Great! Interestingly, the same gene, Pocillopora_acuta_HIv2_RNAseq.g25242.t1 is the top match for all 5 proteins I searched. Pocillopora_acuta_HIv2_TS.g24434.t1 is the second best match for all 5. That is interesting! Pocillopora_acuta_HIv2_RNAseq.g7583.t1 is also worth looking at. And Pocillopora_acuta_HIv2_TS.g21338.t1 matched all three YY1 isoforms.
YinYangs <- c("Pocillopora_acuta_HIv2___RNAseq.g25242.t1",
"Pocillopora_acuta_HIv2___TS.g24434.t1",
"Pocillopora_acuta_HIv2___RNAseq.g7583.t1",
"Pocillopora_acuta_HIv2___TS.g21338.t1")
for (i in 1:length(YinYangs)){
plotCounts(dds, gene=YinYangs[i], intgroup=c("Tissue"),)
}
as.data.frame(resOrdered)[YinYangs,]
## baseMean log2FoldChange lfcSE
## Pocillopora_acuta_HIv2___RNAseq.g25242.t1 2388.43282 -0.65267532 0.8125595
## Pocillopora_acuta_HIv2___TS.g24434.t1 86.14232 -0.16148954 1.0080043
## Pocillopora_acuta_HIv2___RNAseq.g7583.t1 429.08727 0.06121494 0.9629085
## Pocillopora_acuta_HIv2___TS.g21338.t1 277.12637 -0.03463576 1.0095974
## pvalue padj
## Pocillopora_acuta_HIv2___RNAseq.g25242.t1 0.1695296 0.3778825
## Pocillopora_acuta_HIv2___TS.g24434.t1 0.2933826 0.5402220
## Pocillopora_acuta_HIv2___RNAseq.g7583.t1 0.8212358 0.9266210
## Pocillopora_acuta_HIv2___TS.g21338.t1 0.2453329 0.4838418
## query
## Pocillopora_acuta_HIv2___RNAseq.g25242.t1 Pocillopora_acuta_HIv2___RNAseq.g25242.t1
## Pocillopora_acuta_HIv2___TS.g24434.t1 Pocillopora_acuta_HIv2___TS.g24434.t1
## Pocillopora_acuta_HIv2___RNAseq.g7583.t1 Pocillopora_acuta_HIv2___RNAseq.g7583.t1
## Pocillopora_acuta_HIv2___TS.g21338.t1 Pocillopora_acuta_HIv2___TS.g21338.t1
Okay! None of these genes are differentially expressed between the tissues. That is interesting and good to know. Pocillopora_acuta_HIv2___RNAseq.g25242.t1 has the highest basal expression of all the potential isoforms of this transcription factor.
df <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])
DE_05_SwissProt$short_name <- ifelse(nchar(DE_05_SwissProt$ProteinNames) > 80,
paste0(substr(DE_05_SwissProt$ProteinNames, 1, 77), "..."),
DE_05_SwissProt$ProteinNames)
gene_labels <- DE_05_SwissProt %>%
select(query,short_name) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
gene_labels <- Manual %>%
select(query,Heatmap_Label) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
TRP <- Manual %>% filter(grepl("transient", ProteinNames, ignore.case = TRUE))
select <- TRP$query
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 12)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/TRP_DE_SwissProt", width = 6.43, height = 4.25, units = "in", dpi = 300)
annot_tab$short_name <- ifelse(nchar(annot_tab$ProteinNames) > 80,
paste0(substr(annot_tab$ProteinNames, 1, 77), "..."),
annot_tab$ProteinNames)
gene_labels <- annot_tab %>%
select(query,short_name) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
TRP <- annot_tab %>% filter(grepl("transient", ProteinNames, ignore.case = TRUE))
select1 <- TRP$query
select <- match(select1,rownames(vsd))
select <- select[!is.na(select)]
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(select1,gene_labels$query),2], fontsize_row = 6)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/TRP_SwissProt")
gene_labels <- Manual %>%
select(query,Heatmap_Label) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
DE_05_biomin_filtered <- DE_05_biomin %>% left_join(Manual,by="query" ) %>% filter(!(Heatmap_Label %in% c("Myosin-9", "Actin, cytoplasmic")))
select <- DE_05_biomin_filtered$query
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
DE_biomin <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 2)
DE_biomin
save_ggplot(DE_biomin, "../output_RNA/differential_expression/DE_biomin")
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k as needed
# Create a dataframe to manage clusters and reordering
clustered_data <- data.frame(
query = select,
Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
cluster = cluster_assignments
)
# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
arrange(cluster, Heatmap_Label)
# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label
# Generate heatmap with reordered rows and labels
DE_biomin <- pheatmap(
z_scores,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = FALSE, # Disable clustering since rows are pre-ordered
cluster_cols = TRUE,
show_rownames = TRUE,
cutree_cols = 2,
annotation_col = (df %>% select(Tissue)),
annotation_colors = ann_colors,
labels_row = ordered_labels,
fontsize_row = 8
)
save_ggplot(DE_biomin, "../output_RNA/differential_expression/clusters_clean/DE_biomin")
#############################
gene_labels <- Manual %>%
select(query,Heatmap_Label) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
DE_05_Biomin_broc_filtered <- DE_05_Biomin_broc %>% left_join(Manual,by="query" ) %>% filter(!(Heatmap_Label %in% c("Myosin-9", "Actin, cytoplasmic")))
select <- DE_05_Biomin_broc_filtered$query
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
DE_Biomin_broc <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 2)
DE_Biomin_broc
save_ggplot(DE_Biomin_broc, "../output_RNA/differential_expression/DE_Biomin_broc")
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k as needed
# Create a dataframe to manage clusters and reordering
clustered_data <- data.frame(
query = select,
Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
cluster = cluster_assignments
)
# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
arrange(cluster, Heatmap_Label)
# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label
# Generate heatmap with reordered rows and labels
DE_Biomin_broc <- pheatmap(
z_scores,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = FALSE, # Disable clustering since rows are pre-ordered
cluster_cols = TRUE,
show_rownames = TRUE,
cutree_cols = 2,
annotation_col = (df %>% select(Tissue)),
annotation_colors = ann_colors,
labels_row = ordered_labels,
fontsize_row = 8
)
save_ggplot(DE_Biomin_broc, "../output_RNA/differential_expression/clusters_clean/DE_Biomin_broc")
###wnt and hox
WNT <- Manual %>% filter(grepl("wnt", Heatmap_Label, ignore.case = TRUE)|grepl("frizzle", Heatmap_Label, ignore.case = TRUE)|grepl("homeobox", Heatmap_Label, ignore.case = TRUE)|grepl("hox", Heatmap_Label, ignore.case = TRUE)|grepl("forkhead", Heatmap_Label, ignore.case = TRUE))#|grepl("wnt", BiologicalProcess, ignore.case = TRUE))
select <- WNT$query
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 2)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/WNT_Hox_DE_SwissProt")
# Perform clustering
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k for the number of clusters
# Create a dataframe for clustering and label management
clustered_data <- data.frame(
query = select,
Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
cluster = cluster_assignments
)
# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
arrange(cluster, Heatmap_Label)
# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label
# Ensure there is only 1 gap between clusters
#row_breaks <- which(cluster_assignments != cluster_assignments[1]) # Only where cluster switches
# Generate the heatmap with reordered rows and labels
top50_DE <- pheatmap(
z_scores,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = FALSE, # Disable clustering since rows are pre-ordered
cluster_cols = TRUE,
cutree_cols = 2,
annotation_col = (df %>% select(Tissue)),
annotation_colors = ann_colors,
labels_row = ordered_labels,
fontsize_row = 8,
borders_color = "white", # Optional border around clusters
cutree_rows = 2, # Keep row clustering if desired
gaps_row = 24 # Add breaks only between clusters
)
# Save the heatmap
save_ggplot(top50_DE, "../output_RNA/differential_expression/clusters_clean/WNT_Hox_DE_SwissProt")
gene_labels <- Manual %>%
select(query,Heatmap_Label) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
select <- DE_05_Hox$query
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 2)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/Hox_Nvec_DE_SwissProt")
# Perform clustering
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k for the number of clusters
# Create a dataframe for clustering and label management
clustered_data <- data.frame(
query = select,
Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
cluster = cluster_assignments
)
# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
arrange(cluster, Heatmap_Label)
# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label
# Ensure there is only 1 gap between clusters
#row_breaks <- which(cluster_assignments != cluster_assignments[1]) # Only where cluster switches
# Generate the heatmap with reordered rows and labels
top50_DE <- pheatmap(
z_scores,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = FALSE, # Disable clustering since rows are pre-ordered
cluster_cols = TRUE,
cutree_cols = 2,
annotation_col = (df %>% select(Tissue)),
annotation_colors = ann_colors,
labels_row = ordered_labels,
fontsize_row = 8,
borders_color = "white", # Optional border around clusters
cutree_rows = 2, # Keep row clustering if desired
gaps_row = 4 # Add breaks only between clusters
)
# Save the heatmap
save_ggplot(top50_DE, "../output_RNA/differential_expression/clusters_clean/Hox_Nvec_DE_SwissProt")
labels <- Hox_all_res %>% drop_na() %>% pull(def_short)
select <- Hox_all_res %>% drop_na() %>% pull(query)
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = labels, fontsize_row = 8, cutree_rows = 2)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/Hox_Nvec_all_SwissProt")
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k for the number of clusters
# Create a dataframe for clustering and label management
clustered_data <- data.frame(
query = select,
Heatmap_Label = labels,
cluster = cluster_assignments
)
# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
arrange(cluster, Heatmap_Label)
# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label
# Ensure there is only 1 gap between clusters
#row_breaks <- which(cluster_assignments != cluster_assignments[1]) # Only where cluster switches
# Generate the heatmap with reordered rows and labels
top50_DE <- pheatmap(
z_scores,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = FALSE, # Disable clustering since rows are pre-ordered
cluster_cols = TRUE,
cutree_cols = 2,
annotation_col = (df %>% select(Tissue)),
annotation_colors = ann_colors,
labels_row = ordered_labels,
fontsize_row = 8,
borders_color = "white", # Optional border around clusters
cutree_rows = 2, # Keep row clustering if desired
gaps_row = 9 # Add breaks only between clusters
)
# Save the heatmap
save_ggplot(top50_DE, "../output_RNA/differential_expression/clusters_clean/Hox_Nvec_all_SwissProt")
gene_labels <- Manual %>%
select(query,Heatmap_Label) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
select <- unique(DE_05_He_etal$query)
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 2)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/He_etal_Nvec_DE_SwissProt")
# Perform clustering
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k for the number of clusters
# Create a dataframe for clustering and label management
clustered_data <- data.frame(
query = select,
Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
cluster = cluster_assignments
)
# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
arrange(cluster, Heatmap_Label)
# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label
# Ensure there is only 1 gap between clusters
#row_breaks <- which(cluster_assignments != cluster_assignments[1]) # Only where cluster switches
# Generate the heatmap with reordered rows and labels
top50_DE <- pheatmap(
z_scores,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = FALSE, # Disable clustering since rows are pre-ordered
cluster_cols = TRUE,
cutree_cols = 2,
annotation_col = (df %>% select(Tissue)),
annotation_colors = ann_colors,
labels_row = ordered_labels,
fontsize_row = 8,
borders_color = "white", # Optional border around clusters
cutree_rows = 2, # Keep row clustering if desired
gaps_row = 4 # Add breaks only between clusters
)
# Save the heatmap
save_ggplot(top50_DE, "../output_RNA/differential_expression/clusters_clean/He_etal_Nvec_DE_SwissProt")
### DuBuc et al 2018 Wnt/Aboral-Oral Patterning Genes, interest
Nematostella
gene_labels <- Manual %>%
select(query,Heatmap_Label) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
select <- unique(DE_05_DuBuc_etal$query)
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 2)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/DuBuc_etal_Nvec_DE_SwissProt")
# Perform clustering
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k for the number of clusters
# Create a dataframe for clustering and label management
clustered_data <- data.frame(
query = select,
Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
cluster = cluster_assignments
)
# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
arrange(cluster, Heatmap_Label)
# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label
# Ensure there is only 1 gap between clusters
#row_breaks <- which(cluster_assignments != cluster_assignments[1]) # Only where cluster switches
# Generate the heatmap with reordered rows and labels
top50_DE <- pheatmap(
z_scores,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = FALSE, # Disable clustering since rows are pre-ordered
cluster_cols = TRUE,
cutree_cols = 2,
annotation_col = (df %>% select(Tissue)),
annotation_colors = ann_colors,
labels_row = ordered_labels,
fontsize_row = 8,
borders_color = "white"#, # Optional border around clusters
#cutree_rows = 2, # Keep row clustering if desired
#gaps_row = 4 # Add breaks only between clusters
)
# Save the heatmap
save_ggplot(top50_DE, "../output_RNA/differential_expression/clusters_clean/DuBuc_etal_Nvec_DE_SwissProt")
gene_labels <- Manual %>%
select(query,Heatmap_Label) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
mucin <- Manual %>% filter(grepl("mucin", ProteinNames, ignore.case = TRUE)|grepl("toll-", ProteinNames, ignore.case = TRUE)|grepl("ZP", ProteinNames, ignore.case = TRUE)|grepl("lectin", ProteinNames, ignore.case = TRUE)|grepl("nitric", Heatmap_Label, ignore.case = TRUE)) %>% filter(Heatmap_Label !="Cnidocyte marker protein (Collectin-11)")
select <- mucin$query
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 2)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/bacteria_DE_SwissProt")
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k for the number of clusters
# Create a dataframe for clustering and label management
clustered_data <- data.frame(
query = select,
Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
cluster = cluster_assignments
)
# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
arrange(cluster, Heatmap_Label)
# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label
# Generate the heatmap with reordered rows and labels
mucins <- pheatmap(
z_scores,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = FALSE, # Disable clustering since rows are pre-ordered
cluster_cols = TRUE,
cutree_cols = 2,
annotation_col = (df %>% select(Tissue)),
annotation_colors = ann_colors,
labels_row = ordered_labels,
fontsize_row = 8,
borders_color = "white", # Optional border around clusters
cutree_rows = 2, # Keep row clustering if desired
gaps_row = c(24,38) # Add breaks only between clusters
)
# Save the heatmap
save_ggplot(mucins, "../output_RNA/differential_expression/clusters_clean/bacteria_DE_SwissProt")
Manual <- read.csv("../output_RNA/differential_expression/DE_05_Manual_annotation.csv") %>% dplyr::rename("query" = 2, "definition" = 3) %>% arrange(X)
gene_labels <- Manual %>%
select(query,Heatmap_Label) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
sensors <- Manual %>% filter(grepl("TRP", Heatmap_Label, ignore.case = TRUE)|grepl("cellular response to light stimulus", BiologicalProcess, ignore.case = TRUE)|grepl("detection of mechanical stimulus involved in sensory perception", BiologicalProcess, ignore.case = TRUE))
select <- sensors$query
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 3)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/sensors_DE_SwissProt")
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 3) # Adjust k for the number of clusters
# Create a dataframe for clustering and label management
clustered_data <- data.frame(
query = select,
Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
cluster = cluster_assignments
)
# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
mutate(cluster = factor(cluster, levels = c(1, 3, 2))) %>%
arrange(cluster, Heatmap_Label)
# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label
# Generate the heatmap with reordered rows and labels
transporters_heat <- pheatmap(
z_scores,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = FALSE, # Disable clustering since rows are pre-ordered
cluster_cols = TRUE,
cutree_cols = 2,
annotation_col = (df %>% select(Tissue)),
annotation_colors = ann_colors,
labels_row = ordered_labels,
fontsize_row = 8,
borders_color = "white", # Optional border around clusters
cutree_rows = 2, # Keep row clustering if desired
gaps_row = c(10,18) # Add breaks only between clusters
)
# Save the heatmap
save_ggplot(transporters_heat, "../output_RNA/differential_expression/clusters_clean/sensors_DE_SwissProt")
# Create annotation data frame
df <- as.data.frame(colData(dds)[, c("Tissue", "Fragment")])
# Function to generate short names for proteins
generate_short_name <- function(data) {
data %>%
mutate(short_name = ifelse(nchar(ProteinNames) > 60,
paste0(substr(ProteinNames, 1, 57), "..."),
ProteinNames))
}
# Function to create gene labels
create_gene_labels <- function(data) {
data %>%
select(query, short_name) %>%
mutate_all(~ ifelse(is.na(.), "", .)) # Replace NAs with ""
}
# Function to generate z-scores for selected genes
calculate_z_scores <- function(data, selection, vsd_matrix) {
selected_genes <- match(selection, rownames(vsd_matrix))
selected_genes <- selected_genes[!is.na(selected_genes)] # Remove NAs
t(scale(t(assay(vsd_matrix)[selected_genes, ]), center = TRUE, scale = TRUE))
}
# Function to create and save heatmap
create_heatmap <- function(z_scores, labels_row, annotation_col, annotation_colors, output_path) {
heatmap <- pheatmap(z_scores,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = TRUE,
show_rownames = TRUE,
cluster_cols = TRUE,
cutree_cols = 2,
annotation_col = annotation_col,
annotation_colors = annotation_colors,
labels_row = labels_row,
fontsize_row = 6)
save_ggplot(heatmap, output_path)
}
# Process DE_05_SwissProt
DE_05_SwissProt <- generate_short_name(DE_05_SwissProt)
gene_labels <- create_gene_labels(DE_05_SwissProt)
TRP <- DE_05_SwissProt %>% filter(grepl("transient", ProteinNames, ignore.case = TRUE))
select <- TRP$query
z_scores <- calculate_z_scores(DE_05_SwissProt, select, vsd)
create_heatmap(z_scores,
labels_row = gene_labels[match(select, gene_labels$query), 2],
annotation_col = df %>% select(Tissue),
annotation_colors = ann_colors,
output_path = "../output_RNA/differential_expression/TRP_DE_SwissProt")
# Process annot_tab
annot_tab <- generate_short_name(annot_tab)
gene_labels <- create_gene_labels(annot_tab)
TRP <- annot_tab %>% filter(grepl("transient", ProteinNames, ignore.case = TRUE))
TRP <- left_join(TRP, as.data.frame(resOrdered)) %>% filter(!is.na(log2FoldChange))
## Joining with `by = join_by(query)`
select1 <- TRP$query
z_scores <- calculate_z_scores(annot_tab, select1, vsd)
create_heatmap(z_scores,
labels_row = TRP$short_name,
annotation_col = df %>% select(Tissue),
annotation_colors = ann_colors,
output_path = "../output_RNA/differential_expression/TRP_SwissProt")
significant <- ifelse(row.names(z_scores) %in% DE_05$query, "Significant", "Not Significant")
# Add this as a new annotation
row_annotation <- data.frame(Significance = significant)
rownames(row_annotation) <- rownames(z_scores)
row_annotation_colors <- list(Significance = c("Significant" = "red", "Not Significant" = "grey"))
heatmap <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = TRUE,
show_rownames = TRUE,
cluster_cols = TRUE,
cutree_cols = 2,
cutree_rows = 5,
annotation_col = df %>% select(Tissue),
annotation_colors = ann_colors,
annotation_row = row_annotation,
annotation_row_colors = row_annotation_colors,
labels_row = TRP$short_name,
fontsize_row = 12)
save_ggplot(heatmap, "../output_RNA/differential_expression/TRP_SwissProt")
# Process DE_05_SwissProt
DE_05_SwissProt <- generate_short_name(DE_05_SwissProt)
gene_labels <- create_gene_labels(DE_05_SwissProt)
GFP <- DE_05_SwissProt %>% filter(grepl("biolum", BiologicalProcess, ignore.case = TRUE))
select <- GFP$query
z_scores <- calculate_z_scores(DE_05_SwissProt, select, vsd)
create_heatmap(z_scores,
labels_row = gene_labels[match(select, gene_labels$query), 2],
annotation_col = df %>% select(Tissue),
annotation_colors = ann_colors,
output_path = "../output_RNA/differential_expression/GFP_DE_SwissProt")
# Process annot_tab
annot_tab <- generate_short_name(annot_tab)
gene_labels <- create_gene_labels(annot_tab)
GFP <- annot_tab %>% filter(grepl("biolum", BiologicalProcess, ignore.case = TRUE))
select1 <- GFP$query
z_scores <- calculate_z_scores(annot_tab, select1, vsd)
create_heatmap(z_scores,
labels_row = gene_labels[match(select1, gene_labels$query), 2],
annotation_col = df %>% select(Tissue),
annotation_colors = ann_colors,
output_path = "../output_RNA/differential_expression/GFP_SwissProt")
Annot_Pdam <- read.csv("../references/annotation/blastp_Pdam_out.tab", sep = '\t', header = FALSE) %>% select(c(1,2)) %>% dplyr::rename("protein_id" = "V2", "query" = "V1")
Manual_Pdam <- left_join(Manual, Annot_Pdam)
## Joining with `by = join_by(query)`
library(readxl)
larval_aboral_enriched <- read_excel("../output_RNA/marker_genes/RamonMateu_Pdam_larval.xlsx", sheet = "pocillopora_aboral_enriched_05_")
Manual_Pdam_upaboral <- Manual_Pdam %>% filter(log2FoldChange > 0)
inner_join(larval_aboral_enriched, Manual_Pdam_upaboral, by=c("gene ID"="protein_id")) %>% dim()
## [1] 18 26
#18 genes overlap from the 126 in the paper
larval_oral_enriched <- read_excel("../output_RNA/marker_genes/RamonMateu_Pdam_larval.xlsx", sheet = "pocillopora_oral_enriched_05_lf")
Manual_Pdam_uporal <- Manual_Pdam %>% filter(log2FoldChange < 0)
inner_join(larval_oral_enriched, Manual_Pdam_uporal, by=c("gene ID"="protein_id")) %>% dim()
## [1] 6 26
inner_join(larval_oral_enriched, Manual_Pdam_uporal, by=c("gene ID"="protein_id")) %>% View()
#only 6/83 genes overlap
larval_aboral_clusters <- read_excel("../output_RNA/marker_genes/RamonMateu_Pdam_larval_scRNA.xlsx") %>% filter(!is.na(pocillopora))
## New names:
## • `` -> `...8`
## • `` -> `...9`
## • `` -> `...10`
## • `` -> `...11`
larval_aboral_clusters_Pacuta <- inner_join(larval_aboral_clusters, Manual_Pdam, by=c("pocillopora"="protein_id"))
#my genes upregulated in oral tissue are high in mucous cells, cool
After you’ve confirmed your code works as expected, use renv::snapshot() to record the packages and their sources in the lockfile.
renv::snapshot()